Machine learning-assisted wearable sensing systems for speech recognition and interaction

We proposed a wearable wireless flexible skin-attached acoustic sensor (SAAS) that captures vocal organ vibrations and skin movements for voice recognition and human-machine interaction (HMI) in noisy environments. Using piezoelectric micromachined ultrasonic transducers (PMUT) with high sensitivity (-198 dB), wide bandwidth (10 Hz–20 kHz), and excellent flatness (±0.5 dB), the system ensures reliable performance. Flexible packaging enhances comfort, while integration with a Residual Network (ResNet) achieves over 96% accuracy in classifying laryngeal speech features. The system also demonstrated 99.8% sentence recognition accuracy using a deep learning model in various HMI scenarios. SAAS offers a low-cost, easy-to-fabricate, and high-performance solution for voice control, HMI, and wearable electronics.

本研究提出一款可穿戴无线柔性贴肤式声学传感器（skin-attached acoustic sensor，SAAS），可捕获发声器官振动与皮肤运动，用于嘈杂环境下的语音识别与人机交互（human-machine interaction，HMI）。该系统采用灵敏度达-198 dB、带宽覆盖10 Hz~20 kHz且平坦度优异（±0.5 dB）的压电微机械超声换能器（piezoelectric micromachined ultrasonic transducers，PMUT），保障了可靠的工作性能。柔性封装设计提升了佩戴舒适度，结合残差网络（Residual Network，ResNet）后，喉部语音特征分类准确率可达96%以上。此外，该系统在多种人机交互场景中，依托深度学习模型实现了99.8%的语句识别准确率。SAAS为语音控制、人机交互及可穿戴电子设备领域提供了一种低成本、易制备且高性能的解决方案。

搜集汇总

背景与挑战

背景概述

该数据集包含37个Excel文件，记录了基于柔性电子传感器和机器学习技术的语音识别系统研究数据。数据集支持可穿戴无线柔性声学传感器的性能测试结果，该系统在噪声环境下实现了96%的喉部语音特征分类准确率和99.8%的语句识别准确率。